Today, Prof. Boleslaw K. Szymanski presented a talk entitled "Finding Efficient Spreaders for Information Diffusion in Social Networks," at the Department of Computer Science a the University of California, Santa Barbara.

November 07, 2018

Today, Prof. Boleslaw K. Szymanski presented a talk entitled "Finding Efficient Spreaders for Information Diffusion in Social Networks," at the Department of Computer Science a the University of California, Santa Barbara. The talk focuses on recent global events and their poor predictability are often attributed to the complexity of the world event dynamics. In this talk we present a method to improve the predictability of a simple but classic threshold model of contagion spreading in complex social systems. In this model information propagates with certain probability from nodes just activated to their non-activated neighbors. Diffusion cascades can be triggered by activation of even a small set of nodes. We consider the heterogeneity of individuals' susceptibility to new ideas. We investigate numerically and analytically the transition in the behavior of threshold-limited cascades in the presence of multiple initiators as the distribution of thresholds is varied between the two extreme cases of identical thresholds and a uniform distribution. We show that individuals' heterogeneity of susceptibility governs the dynamics, resulting in different sizes of initiators needed for consensus. Furthermore, given the impact of heterogeneity on the cascade dynamics, we introduce two new selection strategies for Influence Maximization. One of them focuses on finding the balance between targeting nodes which have high resistance to adoptions versus nodes positioned in central spots in networks. The second strategy focuses on the combination of nodes for reaching consensus, by targeting nodes which increase the group's influence. Our strategies outperform other existing strategies regardless of the susceptibility diversity and network degree assortativity. Initial activation of seeds is commonly performed in a single stage. We discuss a novel approach based on sequential seeding. We present experimental results comparing single stage and sequential approaches on directed and undirected graphs to the well-known greedy approach to provide the objective measure of the sequential seeding benefits. Surprisingly, applying sequential seeding to a simple degree-based selection leads to higher coverage than achieved by the expensive greedy approach currently considered the best heuristic.